Sequential Monte Carlo methods for parameter estimation in nonlinear state-space models

Authors:
Meng Gao;Hui Zhang
Affiliations:
Yantai Institute of Coastal Zone Research, CAS, Yantai 264003, China;School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China
Venue:
Computers & Geosciences
Year:
2012

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Abstract

Stochastic nonlinear state-space models (SSMs) are prototypical mathematical models in geoscience. Estimating unknown parameters in nonlinear SSMs is an important issue for environmental modeling. In this paper, we present two recently developed methods that are based on the sequential Monte Carlo (SMC) method for parameter estimation in nonlinear SSMs. The first method, which belongs to classical statistics, is the SMC-based maximum likelihood estimation. The second method, belonging to Bayesian statistics, is Particle Markov Chain Monte Carlo (PMCMC). With a low-dimensional nonlinear SSM, the implementations of the two methods are demonstrated. It is concluded that these SMC-based parameter estimation methods are applicable to environmental modeling and geoscience.